TF之DNN：TF利用 784 个神经元的三层全连接的神经网络DNN对MNIST手写数字识别实现98%准确率——Jason niu

# -*- coding: utf-8 -*-

#实现的神经网络共有三层，输入层有 784 个神经元，隐藏层与输出层分别有 500 和 10 个神经元。这所以这样设计是因为 MNIST 的像素为 28×28=784，所以每一个输入神经元对应于一个灰度像素点。批量大小为 100并使用学习率衰减的情况下迭代 10000 步能得到 98.34% 的测试集准确度，
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
 

#加载MNIST数据集
mnist = input_data.read_data_sets("./data/MNIST/", one_hot=True)
INPUT_NODE = 784
OUTPUT_NODE = 10
LAYER1_NODE = 500
BATCH_SIZE = 100
      
# 模型相关的参数
LEARNING_RATE_BASE = 0.8
LEARNING_RATE_DECAY = 0.99
REGULARAZTION_RATE = 0.0001
TRAINING_STEPS = 10000
MOVING_AVERAGE_DECAY = 0.99
 
def inference(input_tensor, avg_class, weights1, biases1, weights2, biases2):
# 使用滑动平均类
    if avg_class == None:
        layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)
        return tf.matmul(layer1, weights2) + biases2
    else:
        layer1 = tf.nn.relu(tf.matmul(input_tensor, avg_class.average(weights1)) + avg_class.average(biases1))
        return tf.matmul(layer1, avg_class.average(weights2)) + avg_class.average(biases2)  
def train(mnist):
    x = tf.placeholder(tf.float32, [None, INPUT_NODE], name='x-input')
    y_ = tf.placeholder(tf.float32, [None, OUTPUT_NODE], name='y-input')   
    # 生成隐藏层的参数。
    weights1 = tf.Variable(tf.truncated_normal([INPUT_NODE, LAYER1_NODE], stddev=0.1))
    biases1 = tf.Variable(tf.constant(0.1, shape=[LAYER1_NODE]))
    # 生成输出层的参数。
    weights2 = tf.Variable(tf.truncated_normal([LAYER1_NODE, OUTPUT_NODE], stddev=0.1))
    biases2 = tf.Variable(tf.constant(0.1, shape=[OUTPUT_NODE]))
   
    # 计算不含滑动平均类的前向传播结果
    y = inference(x, None, weights1, biases1, weights2, biases2) 
    # 定义训练轮数及相关的滑动平均类 
    global_step = tf.Variable(0, trainable=False)
    variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)
    variables_averages_op = variable_averages.apply(tf.trainable_variables())
    average_y = inference(x, variable_averages, weights1, biases1, weights2, biases2)
    # 计算交叉熵及其平均值
    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))
    cross_entropy_mean = tf.reduce_mean(cross_entropy)
    # 定义交叉熵损失函数加上正则项为模型损失函数
    regularizer = tf.contrib.layers.l2_regularizer(REGULARAZTION_RATE)
    regularaztion = regularizer(weights1) + regularizer(weights2)
    loss = cross_entropy_mean + regularaztion
    # 设置指数衰减的学习率。
    learning_rate = tf.train.exponential_decay(
        LEARNING_RATE_BASE,
        global_step,
        mnist.train.num_examples / BATCH_SIZE,
        LEARNING_RATE_DECAY,
        staircase=True)
    # 随机梯度下降优化器优化损失函数,使用梯度下降优化器来优化权重。然而，TensorFlow 中还有很多优化器，最常用的是 GradientDescentOptimizer、AdamOptimizer 和 AdaGradOptimizer。
    train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)
    # 反向传播更新参数和更新每一个参数的滑动平均值
    with tf.control_dependencies([train_step, variables_averages_op]):
        train_op = tf.no_op(name='train')
    # 计算准确度
    correct_prediction = tf.equal(tf.argmax(average_y, 1), tf.argmax(y_, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# 初始化会话并开始训练过程。
    with tf.Session() as sess:
        tf.global_variables_initializer().run()
        validate_feed = {x: mnist.validation.images, y_: mnist.validation.labels}
        test_feed = {x: mnist.test.images, y_: mnist.test.labels} 
        # 循环地训练神经网络。
        for i in range(TRAINING_STEPS):
            if i % 1000 == 0:
                validate_acc = sess.run(accuracy, feed_dict=validate_feed)
                print("After %d training step(s), validation accuracy using average model is %g " % (i, validate_acc))
            xs,ys=mnist.train.next_batch(BATCH_SIZE)
            sess.run(train_op,feed_dict={x:xs,y_:ys})
        test_acc=sess.run(accuracy,feed_dict=test_feed)
        print(("After %d training step(s), test accuracy using average model is %g" %(TRAINING_STEPS, test_acc)))
        
     
avg_class = None        
train(mnist)